Cardinal temperatures variability within a tropical japonica rice diversity panel

• Main Authors: Lauriane Rouan, Alain Audebert, Delphine Luquet, Sandrine Roques, Audrey Dardou, Eric Gozé
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2018-07-01
• Series: Plant Production Science
• Subjects: Oryza sativa; base temperature; crop model; leaf growth; hierarchical modeling; error propagation
• Online Access: http://dx.doi.org/10.1080/1343943X.2018.1467733
• ISSN: 1343-943X; 1349-1008

Air temperature is one of the most critical climatic factors controlling rice growth, development, and production in current and future climatic scenarii predicting increasingly frequent situations of extreme and/or fluctuating temperatures. With its large spectrum of geographical origins and cropping areas, one can credit tropical japonica rice subspecies of a probable genetic diversity of its response to air temperature, which is of major interest for the breeding of better adapted rice varieties. A panel of 195 rice accessions (175 japonica plus 20 reference cultivars) was studied in controlled environment to estimate cardinal (base, optimum, and maximum) temperatures based on the monitoring of the elongation rate (LERmax) of the sixth leaf on the main stem in response to six fixed thermal treatments ranging from 16 to 35 °C. A dedicated statistical framework was elaborated for estimating LERmax, cardinal temperature and related uncertainties. Developed statistical framework enhanced the precision of cardinal temperatures estimated compared to previously reported methods, especially for base temperature. Maximum temperature was trickier to estimate and will require further studies. A significant genotypic variability for base and optimal temperature was pointed out, suggesting tropical japonica subspecies represents a relevant genetic pool to breed for rice genotypes adapted to various thermal situations. These results also suggested that using genotype-dependent cardinal temperature values should enhance the way crop growth models account for genotype × environment interactions hence their predictive value in current and future climatic conditions.